In [1]:
# Load dataset and import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly .express as px
In [2]:
df = pd.read_csv(r"C:\Users\jayant soni\Downloads\penguins_lter (1).csv")
df.head()
Out[2]:
studyName Sample Number Species Region Island Stage Individual ID Clutch Completion Date Egg Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Sex Delta 15 N (o/oo) Delta 13 C (o/oo) Comments
0 PAL0708 1 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N1A1 Yes 11/11/07 39.1 18.7 181.0 3750.0 MALE NaN NaN Not enough blood for isotopes.
1 PAL0708 2 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N1A2 Yes 11/11/07 39.5 17.4 186.0 3800.0 FEMALE 8.94956 -24.69454 NaN
2 PAL0708 3 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N2A1 Yes 11/16/07 40.3 18.0 195.0 3250.0 FEMALE 8.36821 -25.33302 NaN
3 PAL0708 4 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N2A2 Yes 11/16/07 NaN NaN NaN NaN NaN NaN NaN Adult not sampled.
4 PAL0708 5 Adelie Penguin (Pygoscelis adeliae) Anvers Torgersen Adult, 1 Egg Stage N3A1 Yes 11/16/07 36.7 19.3 193.0 3450.0 FEMALE 8.76651 -25.32426 NaN
In [3]:
#to detect outlier 
df.describe()
Out[3]:
Sample Number Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Delta 15 N (o/oo) Delta 13 C (o/oo)
count 344.000000 342.000000 342.000000 342.000000 342.000000 330.000000 331.000000
mean 63.151163 43.921930 17.151170 200.915205 4201.754386 8.733382 -25.686292
std 40.430199 5.459584 1.974793 14.061714 801.954536 0.551770 0.793961
min 1.000000 32.100000 13.100000 172.000000 2700.000000 7.632200 -27.018540
25% 29.000000 39.225000 15.600000 190.000000 3550.000000 8.299890 -26.320305
50% 58.000000 44.450000 17.300000 197.000000 4050.000000 8.652405 -25.833520
75% 95.250000 48.500000 18.700000 213.000000 4750.000000 9.172123 -25.062050
max 152.000000 59.600000 21.500000 231.000000 6300.000000 10.025440 -23.787670
In [4]:
#Missing value imputation
df.isnull().sum()
Out[4]:
studyName                0
Sample Number            0
Species                  0
Region                   0
Island                   0
Stage                    0
Individual ID            0
Clutch Completion        0
Date Egg                 0
Culmen Length (mm)       2
Culmen Depth (mm)        2
Flipper Length (mm)      2
Body Mass (g)            2
Sex                     10
Delta 15 N (o/oo)       14
Delta 13 C (o/oo)       13
Comments               318
dtype: int64
In [5]:
df.notnull().sum()
Out[5]:
studyName              344
Sample Number          344
Species                344
Region                 344
Island                 344
Stage                  344
Individual ID          344
Clutch Completion      344
Date Egg               344
Culmen Length (mm)     342
Culmen Depth (mm)      342
Flipper Length (mm)    342
Body Mass (g)          342
Sex                    334
Delta 15 N (o/oo)      330
Delta 13 C (o/oo)      331
Comments                26
dtype: int64
In [6]:
df.isna().mean()*100
Out[6]:
studyName               0.000000
Sample Number           0.000000
Species                 0.000000
Region                  0.000000
Island                  0.000000
Stage                   0.000000
Individual ID           0.000000
Clutch Completion       0.000000
Date Egg                0.000000
Culmen Length (mm)      0.581395
Culmen Depth (mm)       0.581395
Flipper Length (mm)     0.581395
Body Mass (g)           0.581395
Sex                     2.906977
Delta 15 N (o/oo)       4.069767
Delta 13 C (o/oo)       3.779070
Comments               92.441860
dtype: float64
In [7]:
fig = px.histogram(df, x='Culmen Depth (mm)')
fig.show()
In [8]:
fig=px.box(df,y=('Flipper Length (mm)'))
fig.show()
In [9]:
def find_outliers_IQR(df):
    q1=df.quantile(0.25)
    q3=df.quantile(0.75)
    IQR=q3-q1
    outliers = df[((df<(q1-1.2*IQR)) | (df>(q3+1.2*IQR)))]
    return outliers
In [10]:
outliers = find_outliers_IQR(df["Culmen Depth (mm)"])
print("number of outliers: "+ str(len(outliers)))
print("max outlier value: "+ str(outliers.max()))
print("min outlier value: "+ str(outliers.min()))
outliers

number of outliers: 0
max outlier value: nan
min outlier value: nan
Out[10]:
Series([], Name: Culmen Depth (mm), dtype: float64)
In [11]:
#to find the correlation amang 
df.corr(method ='pearson')
Out[11]:
Sample Number Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Delta 15 N (o/oo) Delta 13 C (o/oo)
Sample Number 1.000000 -0.236356 -0.022352 0.040849 -0.007042 0.006952 -0.488690
Culmen Length (mm) -0.236356 1.000000 -0.235053 0.656181 0.595110 -0.059759 0.189025
Culmen Depth (mm) -0.022352 -0.235053 1.000000 -0.583851 -0.471916 0.605874 0.429933
Flipper Length (mm) 0.040849 0.656181 -0.583851 1.000000 0.871202 -0.507787 -0.376223
Body Mass (g) -0.007042 0.595110 -0.471916 0.871202 1.000000 -0.537888 -0.374638
Delta 15 N (o/oo) 0.006952 -0.059759 0.605874 -0.507787 -0.537888 1.000000 0.570615
Delta 13 C (o/oo) -0.488690 0.189025 0.429933 -0.376223 -0.374638 0.570615 1.000000
In [137]:
df.corr(method ='kendall')
Out[137]:
Sample Number Culmen Length (mm) Culmen Depth (mm) Flipper Length (mm) Body Mass (g) Delta 15 N (o/oo) Delta 13 C (o/oo)
Sample Number 1.000000 -0.141843 -0.029314 0.040947 0.009671 0.009710 -0.254544
Culmen Length (mm) -0.141843 1.000000 -0.122850 0.483345 0.433359 -0.064553 0.097830
Culmen Depth (mm) -0.029314 -0.122850 1.000000 -0.281894 -0.195070 0.424881 0.293145
Flipper Length (mm) 0.040947 0.483345 -0.281894 1.000000 0.660467 -0.316815 -0.230067
Body Mass (g) 0.009671 0.433359 -0.195070 0.660467 1.000000 -0.372535 -0.254730
Delta 15 N (o/oo) 0.009710 -0.064553 0.424881 -0.316815 -0.372535 1.000000 0.362955
Delta 13 C (o/oo) -0.254544 0.097830 0.293145 -0.230067 -0.254730 0.362955 1.000000
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